268results about "Auscultation instruments" patented technology

The invention discloses an abnormal data detection method, device and system based on electrocardio and heart sound, and the method comprises the steps: synchronously collecting an electrocardio signal and a heart sound signal, and carrying out the first preprocessing and the second preprocessing, thereby obtaining electrocardio data and heart sound data; inputting the electrocardio data and the heart sound data into a double-flow convolutional network for feature extraction to obtain an electrocardio feature sequence and a heart sound feature sequence; inputting the electrocardio feature sequence and the heart sound feature sequence into a cross-modal fusion decision model to obtain pooling fusion enhanced features, thereby effectively performing time sequence alignment, fusion and enhancement on the electrocardio feature sequence and the heart sound feature sequence; the score of the pooling fusion enhanced feature is obtained by using a nonlinear scoring formula, whether abnormal data exist in the electrocardiosignal and the heart sound signal of the detected person or not is determined through the score of the pooling fusion enhanced feature, and the method has important significance on early warning, curative effect evaluation and the like of patients with cardiovascular and cerebrovascular diseases.

The invention discloses a thoracic cavity heart sound signal non-contact extraction system and method based on structured light. The system comprises a structured light projection module, an image acquisition module, an image processing and displacement extraction module and a signal processing and trigger point identification module, structured light is projected on the surface of the chest of a human body to form a regular pattern, and a high-speed image acquisition means is utilized to record micro displacement change generated by light spots under cardiac drive. Thoracic cavity vibration information is extracted through an image processing technology, periodic feature points corresponding to a cardiac rhythm are recognized in combination with a time sequence analysis strategy, a thoracic cavity heart sound signal is extracted, rhythm analysis is conducted, and a cardiac trigger point is output to serve as a trigger signal for synchronous control of medical equipment. The technical problems of signal distortion, equipment interference, potential safety hazards, inconvenience in use and the like in a strong electromagnetic environment in a traditional contact mode are solved, and a high-precision synchronous trigger signal is provided for dynamic imaging.

An anti-snoring system comprising an adjustable bed having a sleeping surface that may be mechanically raised or lowered, and a control module adapted to receive commands from a source external to the adjustable bed; a mobile device in direct or indirect communication with the control module in the adjustable bed, and wherein the mobile device has sound recording capabilities; a mobile application resident on the mobile device, wherein the mobile application includes a sound classification machine learning model that includes an artificial intelligence or neural network operative to determine whether or not a person on the sleep surface is snoring, and wherein upon a determination that the person is snoring and has been snoring for a predetermined period of time, the mobile application instructs the control module to raise or adjust the sleeping surface to a height or position that will discourage the person from snoring.

The invention relates to the technical field of respiratory lung sound recognition, in particular to a respiratory lung sound auxiliary recognition method and system for clinical nursing. The invention provides a respiratory lung sound auxiliary identification method and system for clinical nursing, solves the technical problems of data scarcity, complex noise interference and insufficient cross-device generalization ability in lung sound signals through generative data enhancement and cross-modal migration, and combines self-supervised comparative learning and causal feature discovery to identify the respiratory lung sound. Multi-source signal feature fusion and noise robustness improvement are realized, a real-time noise environment is dynamically adapted, the limitation that a traditional method depends on annotation data and feature extraction is easily influenced by hybrid noise is broken through, a full-link closed-loop system from synthetic data generation, cross-modal alignment to causal-driven decision is constructed, and multi-source signal feature fusion and noise robustness improvement are realized. And the accuracy and clinical applicability of clinical lung sound analysis are effectively improved.

The present application relates to the technical field of health monitoring, in particular to a kind of infant asphyxia risk autonomous monitoring method and system based on laryngeal respiratory sound, comprising: utilize high sensitivity MEMS microphone to collect the airflow and respiratory sound signal of infant larynx, and utilize micro accelerometer to monitor the thoracic movement data of infant synchronously;The airflow and respiratory sound signal are denoising, and the denoising respiratory sound signal is obtained;The denoising respiratory sound signal is separated according to respiratory event Signal, and is enhanced and combined into respiratory sound data set;Respiratory sound feature is extracted in respiratory sound data set, and asphyxia anomaly classification model is constructed based on respiratory sound feature by machine learning model, and dynamic baseline model that adapts individual respiratory difference is established according to respiratory sound feature.The present application utilizes the hardware device of high sensitivity MEMS microphone and micro accelerometer and artificial intelligence model to combine, realizes the use of laryngeal respiratory sound signal in home environment and autonomously completes infant asphyxia risk identification and early warning.

The invention relates to the technical field of audio signal processing. The invention provides a method, system and device for predicting pulmonary nodules through sound and a storage medium. The method comprises the following steps: in a quiet test environment, directionally collecting a sound signal of a patient through an audio sensor; wherein the sound signals comprise breath sounds, cough sounds, vowels and sentences; performing noise reduction processing on the collected sound signals to obtain a noise reduction data set; extracting multi-dimensional voiceprint features from the noise reduction data set, and constructing a sound feature matrix; and performing multi-modal fusion analysis on the sound feature matrix and clinical data of the patient, inputting the sound feature matrix and the clinical data into a sound prediction model optimized by a focus loss function, and outputting a pulmonary nodule risk prediction result and a visual diagnosis report. The problems that the misdiagnosis and missed diagnosis rate is high, the radiation risk is large and the equipment dependence is strong when the pulmonary nodules are examined by the existing iconography, and the characteristic analysis is single, the noise immunity is poor and the screening accuracy is insufficient in the sound detection technology are solved.

According to some embodiments, an ear-worn device, e.g., a hearing aid, is provided that operates to monitor the health of a wearer of the ear-worn device based on an audio signal detected by the ear-worn device. In some embodiments, a method of monitoring the health of the wearer of the ear-worn device includes: detecting the audio signal with a microphone of the ear-worn device; processing the detected audio signal using a processor of the ear-worn device; and outputting, from an output signal generator of the ear-worn device, a generated output audio signal. In some embodiments, processing the detected audio signal includes: extracting data indicative of a health event associated with the wearer by processing the detected audio signal with a machine learning model; generating the output audio signal based on the detected audio signal; and outputting the data indicative of the health event.

Disclosed are devices, systems and related methods for wearable devices that sense various anatomical conditions and / or metrics of a wearer, including measuring and optionally transmitting this data to a computing device which analyzes the data to determine various physiological conditions within and / or in proximity to a wearer's body. The device comprises a central body having a first sensor and a body piercing post with a second sensor, wherein the sensors measure a first and second physiological condition respectively of the wearer continuously or periodically using specified time intervals.

The invention relates to the technical field of health monitoring, in particular to an infant suffocation risk autonomous monitoring method and system based on laryngeal breathing sound, and the method comprises the steps: collecting airflow and breathing sound signals of the throat of an infant through a high-sensitivity MEMS microphone, and synchronously monitoring thoracic motion data of the infant through a miniature accelerometer; performing noise reduction processing on the airflow and the breathing sound signal to obtain a denoised breathing sound signal; performing signal separation on the denoised breathing sound signals according to a breathing event, and enhancing and combining the denoised breathing sound signals into a breathing sound data set; extracting breath sound features from the breath sound data set, constructing a suffocation anomaly classification model based on the breath sound features through a machine learning model, and establishing a dynamic baseline model adapted to individual breath differences according to the breath sound features; hardware devices of a high-sensitivity MEMS microphone and a micro accelerometer are combined with an artificial intelligence model, and infant asphyxia risk identification and early warning are autonomously completed in a home environment by using laryngeal respiration sound signals.

The invention discloses a multi-modal electrocardiosignal and arrhythmia recognition system and method. The system comprises a signal acquisition module, a preprocessing module, a space-time alignment module, a feature extraction module, a self-adaptive fusion module, a multi-modal fusion module, a classification risk assessment module and a visual report module. The recognition method comprises the following steps: S1, synchronously acquiring an electrocardiosignal and a heart sound signal, and preprocessing the electrocardiosignal and the heart sound signal; s2, establishing a time corresponding relation between the electrical activity and the mechanical activity; s3, calculating a mechanical shrinkage efficiency index; s4, evaluating the quality of the electrocardiosignal and the heart sound signal in real time, and dynamically adjusting contribution weights of the electrocardiosignal and the heart sound signal in a fusion process according to the quality; s5, adopting a multi-head attention mechanism to generate a joint feature vector for classification; s6, inputting the joint feature vector into a classification network model, and outputting an arrhythmia type and a quantitative risk level at the same time by the classification network model; and S7, generating a visual atlas and a structured diagnosis report.

A device for cardiac monitoring, comprising a processor having a digital signal processing unit, the digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having one or more sensors, the processor including a program having executable instructions configured, when executed on the processor, to cause the processor to perform the following steps: synchronizing the processed signals of each sensor assembly, mapping the synchronized signals as waveforms of each sensor, calculating at least one cardiac function parameter as data values ​​with predetermined waveform amplitudes and predefined time intervals, and performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine a cardiac condition.

An anti-snoring system comprising an adjustable bed having a sleeping surface that may be mechanically raised or lowered, and a control module adapted to receive commands from a source external to the adjustable bed; a mobile device in direct or indirect communication with the control module in the adjustable bed, and wherein the mobile device has sound recording capabilities; a mobile application resident on the mobile device, wherein the mobile application includes a sound classification machine learning model that includes an artificial intelligence or neural network operative to determine whether or not a person on the sleep surface is snoring, and wherein upon a determination that the person is snoring and has been snoring for a predetermined period of time, the mobile application instructs the control module to raise or adjust the sleeping surface to a height or position that will discourage the person from snoring.

A system is provided for detecting bruxism or a temporomandibular joint disorder. A pressure sensor senses an external ear canal air pressure and bruxism or a temporomandibular joint disorder are identified from the sensed ear canal air pressure. An output signal indicates the detected bruxism or temporomandibular joint disorders so that remedial action can be taken.

Reinforced surgical table mattresses or pads and methods of manufacture are disclosed. A layer of polymeric foam can be inserted into a shell formed from one or more sheets of flexible polymeric film material laminated to a fabric and welded together to create a water-proof shell. At least one layer of hotmelt glue film can be positioned inside the shell between the bottom side of the foam and the fabric of the shell. Heat can be applied to the outside of the shell at a temperature above the melting point of the hotmelt glue film and below that of the shell material, melting the glue film and bonding the fabric of the shell to an adjacent layer of material when cooled.

According to an aspect, an improved modular wearable medical device for monitoring a patient's ECG signals and providing therapeutic shocks in the event of a cardiac arrhythmia is provided. The device includes a garment, a plurality of ECG sensing electrodes that are permanently coupled at first predetermined locations on the garment, at least one pocket or receptacle disposed on the garment and configured to removably secure a module with device electronics within a housing to the garment, a plurality of wires or cables permanently integrated into the garment and operably coupling the plurality of permanently coupled ECG sensing electrodes with the sensor interface module, a plurality of therapy electrodes removably coupled to second predetermined locations on the garment, gel deployment circuitry, one or more capacitors, and a therapy control module configured to control the provision of the therapeutic shocks.

A device for heart monitoring, the device comprising a processor having a digital signal processing unit, wherein the digital signal processing unit is configured to receive and process physiological signals from at least one sensor assembly having one or more sensors. The processor comprises a program having executable instructions that when executed on the processor, the processor is configured to execute the steps of: synchronising processed signals for each sensor assembly; mapping the synchronised signals as waveforms for each sensor; predetermining waveform amplitudes and predetermined time intervals to calculate at least one cardiac function parameter as a data value; and performing a step of differential analysis between the calculated data value and a set of referencing cardiac health parameters to determine a condition of the heart.

The present invention discloses a method in a processor associated with a respiratory therapy device for identifying a component of an airpath coupled to the respiratory therapy device, the method comprising: processing a sound signal over a finite time window to obtain a cepstrum, the sound signal representing a sound in the airpath, wherein the finite time window is chosen to minimize a varying effect of a breathing cycle on acoustic characteristics of the airpath; separating an acoustic signature from the cepstrum; repeating the processing and separating at least once to generate a plurality of acoustic signatures with a plurality of finite time windows comprising the finite time window, combining the plurality of acoustic signatures into a combined acoustic signature; and identifying the component.

An audio detection system includes a mouthpiece configured to detect vibrations occurring at a subject's head, generate a signal based on the vibrations, and transmit the signal. The audio detection system also includes a signal processing module configured to receive the signal from the mouthpiece as an audio input signal, generate an output signal based on the audio input signal, and transmit the output signal. A method of detecting an abnormal condition experienced by a subject includes detecting vibrations within the subject's head at a mouthpiece positioned within the subject's mouth, generating a signal based on the vibrations at the mouthpiece, transmitting the signal from the mouthpiece to a signal processing module, receiving the signal as an audio input signal at the signal processing module, generating an output signal based on the audio input signal at the signal processing module, and transmitting the output signal.

A novel and advantageous system and method for assessing respiration is provided. Particularly, a novel and advantageous system and method for tracking and assessing respiration is provided. More particularly, a novel and advantageous system and method for guiding, tracking, and assessing respiration and providing real-time feedback on a user's breathing is provided.

Provided herein are methods of treatment, including methods of treating subjects having or at risk of having or having a viral infection, and specifically a SARS-CoV-2 viral infection. The methods provided include the administration of 4-methylumbelliferone (4-MU), palmitoylethanolamide (PEA), reservatrol, fisetin, H2, nebulized hyaluronidase or combinations thereof. Also provided herein are a respiratory assistance device, methods of generating a customized respiratory assistance device, methods of treating a coronavirus infection, and methods of inhibiting a coronavirus infectivity, virulence and / or spread.

Systems, devices, and methods including a processor having addressable memory, where the processor is configured to: collect, via a set of sensors, biological signals related to patient status; collect, via an environmental monitoring device, environmental data associated with an environment of the patient; store, locally via a secure encrypted local database, the collected biological signals and the collected environmental data; detect an abnormality in the collected biological signals; increase measurement frequency of the biological signals by the set of sensors based on the detected abnormality; detect an error in the collected biological signals; determine whether the detected error in the collected biological signals of the current reading is due to environmental factors; correct the determined error in the collected biological signals; and transmit the error corrected collected biological signals as encrypted data.

An in-ear device (IED) of a wearable system captures audio data from within the ear canal of a user indicative of the user's heartbeat. The IED when worn occludes the user's ear canal, causing amplification of low frequency sounds due to the occlusion effect, and improving the ability of the acoustic sensor to detect the user's heartbeat sounds. The wearable system classifies the audio data to identify portions of the audio data corresponding to a first heart sound and a second heart sound, which correspond to different portions of the heartbeat of the user. The wearable system estimates a blood pressure level of the user based upon the identified heart sounds.

Disclosed herein are systems and methods for providing feeding reinforcement in real-time or near real-time based on physiological sensor data acquired during feeding. In some examples, music reinforcement is rendered when one or more feeding features indicative of one or more feeding behaviors are detected using the physiological sensor data. When at least one feature is not detected, the music reinforcement is stopped. In this way, contingent reinforcement is provided in real-time or near real-time based on detection of the one or more feeding behaviors to encourage and improve independent feeding behavior.

A method (100) for obtaining a respiratory-related sound (RRS) (160) originating from a target patient is disclosed, the method comprising the steps of obtaining an input audio recording of a sleep environment of the target patient (110, 111), obtaining a respiratory trace of respiration of the target patient (150), identifying (120) RRSs (130) in the input audio recording, and selecting (140) the RRS (160) originating from the target patient from the RRSs based on the respiratory trace. The selection further comprises determining a first subset and / or a second subset of RRSs having a correspondingly high probability of originating from the target patient and / or a low probability of originating from the target patient, training a classifier based on the first subset and / or the second subset to select RRSs originating from the target patient, and selecting the RRS (160) originating from the target patient by the trained classifier.

The embodiments of the present application disclose a system and a method, the system comprising: at least one storage device, the at least one storage device storing computer instructions; and at least one processor, the at least one processor being in communication with the at least one storage device, and when executing the stored computer instructions, the at least one processor causes the system to perform operations of: acquiring a sensing signal of at least one sensing device; identifying a signal feature of the sensing signal; and determining an operation of a target object associated with the at least one sensing device based on the signal feature.

The system, method, and apparatus includes a platform having a transparent material defining a first measurement surface. A light surface is disposed to provide light into the transparent material and generate a frustrated total internal reflection (FTIR) event in response to contact at the first measurement surface. The system also includes one or more wearable devices having a base material operable to cover a portion of a human body. An inner surface of the base material defines a second measurement surface. Further, one or more sensors disposed on the base material are operable to collect data at the second measurement surface. Further, the health parameter monitoring system presents displacement motion regimen information on a display screen, such as a virtual reality (VR) headset. The system collects first health parameter data from the first measurement surface based on the FTIR event; and / or collects second health parameter data from the second measurement surface.

An artificial intelligence-based system and method for scalable screening of individuals for respiratory infection, such as COVID-19. The system is trained to distinguish distinct latent features of cough sounds produced by a COVID-19 infected person from cough sounds produced by patients suffering from any other respiratory infection or involuntary cough sounds produced by a healthy person. Cough sound samples from individuals can be remotely collected and evaluated by the system for likelihood of the COVID-19 infection. Additionally, images of affected body parts, biomarkers, metadata, and other respiratory sound samples can also be used for screening.

A ventilation monitoring system for assisting in proper placement of an endotracheal tube in a subject includes: a capnography sensor configured to be placed in fluid communication with the endotracheal tube and to provide information representative of the subject's breath; and a processor in communication with the capnography sensor. The processor is configured to provide an indication of proper endotracheal tube placement when (1) a first indication of the subject's breath and a positive result of a first auscultation are identified within a first predetermined time period, and (2) a second indication of the subject's breath and a positive result of a second auscultation are identified within a second predetermined time period. The first auscultation includes auscultation of a subject's left lung, right lung, left axillary region, right axillary region, or abdomen. The second auscultation includes auscultation of another region of the subject different from the first auscultation.

The invention discloses a wearable intelligent sensing abdominal pain monitoring device and system, and belongs to the technical field of child medical health monitoring. The method comprises the steps that a monitoring range is constructed, symptom parameters are collected, and internal invisible symptom parameters and external invisible symptom parameters are generated; internal invisible symptom parameters are obtained, borborygmus core features are extracted, the intestinal tract abnormal reaction degree is obtained, and the intestinal tract abnormal reaction degree is subjected to score processing; according to related data acquired by the wearable intelligent sensing abdominal pain monitoring device, the device gets rid of dependence on subjective description of child patients completely on the basis of evaluation of physiological indexes such as borborygmus and abdominal muscle tension, is extremely high in adaptability to young children and children with language development retardation, can generate an evaluation report once in a short time, can dynamically track pain changes, and has a good application prospect. And the accuracy of pain grade judgment and disease cause pointing is improved through matching of a double-index fusion algorithm and a clinical database.